In this assignment, we evaluated the potential financial losses associated with vehicle damage in the imposing future of sea level rise and flooding. As we are faced with impacts of what is likely be irreversible climate change, it is important we are prepared. For our analysis we considered Foster City, whose most extreme flood extent considered (50cm sea level rise, 100-year flood) can be seen below.
Within Foster City, we chose to consider census block groups (CBGs) 060816080011 and 060816080012, seen bellow.
The two block groups were chosen because of their significant discrepancy in households hose income over the last 12 months places under the poverty line according to the 2019 American Community Survey. These rates can be seen in the table below. Notably the southern CBG ending in 1 and consisting of more single-family homes, has no families reported as in poverty, but the norther CBG consisting of more apartments is at nearly 20% of households in poverty. I personally found this large difference across such a small geographical space shocking.
| Census Block Group | Total Households | Households below the Poverty Line |
|---|---|---|
| 060816080011 | 644 | 0 |
| 060816080012 | 456 | 79 |
A key fundamental of risk analysis requires understanding exposure, in this case the number of vehicles which may be impacted in the communities of interest. A total count of vehicles broken down by household tenure is shown in the table below. It is worth noting the CBG which is facing higher frequency of poverty also has significantly higher frequency of limited transportation, having one or no vehicles.
For the purposes of analysis vehicle counts were collected via and ACS and distributed across block group populations and then buildings within block based on 2020 decennial census data. This distribution of 2020 vehicles can be seen in the map bellow by selecting individual buildings. Vehicle counts were increased by a percentage in each following decade according estimated EMFAC counts of passenger vehicles and lightweight trucks.
| cbg | tenure | Total Vehicle Count | No vehicle available | 1 vehicle available |
|---|---|---|---|---|
| 060816080011 | Owner occupied: | 1357 | 0 | 187 |
| 060816080011 | Renter occupied: | 275 | 0 | 89 |
| 060816080012 | Owner occupied: | 22 | 9 | 0 |
| 060816080012 | Renter occupied: | 908 | 38 | 360 |
Nine flood scenarios were considered: 0, 25, 50 cm of sea level rise each paired with 3 floods, 1, 20, 100 year storms. For our area of interest only four combinations lead to flooding reaching vehicles. We determined if flooding reach vehicles based on the assumption that vehicles sat at the same elevation as the buildings they are associated with.
The likelihood of vehicle damage is based on two elements, the likelihood of a given storm combination in a given year (captured by RCP 4.5 and storm return periods in this analysis), and the damage a vehicle would take given a flood level. The first factor is not captured in the below plots, but the percent damage associated with certain flood depths is shown. This relationship between damage and flooding is based on assuming all vehicles are sedans. In the plots, this relationship is shown with black dots and the actual occurrence in our considered area is shown in the blue highlight. Here, we can see once again that flooding doesn’t occur in all scenarios, though the lowest scenario with flooding, sea level rise of 25 and 100 year storm, has a small enough proportion of flooding that it is not captured by the plot below. The most extreme sea level rise scenario unsurprisingly faces the most flooding, which can be seen in the lower plot, and can lead to extensive damage.
Actual damage costs were assigned based on assuming all vehicles are valued as a new sedan at $25,000 according to Kelly Blue Book. Below can be seen average annual loss at each decade year including the change between 2050 and 2020. It should be noted that when looking at the per building plot, the northern CBG has much higher values because of the presence of apartment buildings which have many vehicles associated with it. On visual inspection there are not a lot of areas which glaringly stick out, other than buildings directly around an inlet off the river, which is to be expected to a degree.
It should be noted the map above demonstrating unweighted averages fails to capture the number of vehicles, and only considers what the AAL of single vehicle associated with each building would be. The presence of apartments where many vehicles are associated with a building makes this a significant shortcoming, but due to varied vehicle numbers across years, I was not able to figure out how to capture this influence while still giving a digestible number.
In consideration of the total losses plot, it is worth considering that in reality, people may move their vehicles given warning, but in this analysis, we operated under the assumption no vehicles were moved.
I think this analysis presents an interesting dichotomy between renters and owners, as renters likely have less accrued wealth, since land ownership really opens avenues to sustained wealth, but may also accrue less costs due to not having to pay for building damages. That being said, what sticks out most to me is the higher impact of floods on the northern CBG. This can be seen in both the higher CBG average per vehicle AAL as well as in the CBG AAL total, where despite having a lower dollar amount costs (being ~75% of south), the difference in vehicles (being 55% of south) is not proportion and is larger than this cost reduction and we can see here the cost is higher as well.
Additionally, the northern CBG bearing a larger impact is further concerning due to the prevalence of poverty in this area which indicates its residents already lack digressional spending income. This is further emphasized by the higher frequency of no vehicle and single vehicle households in this area. These households are especially vulnerable since they have a high dependency on either a single vehicle or other modes of transportation like public transit which may face issues in recovery as a larger system. Without having a backup form of transportation, these households will likely face an exceedingly challenging time recovering as they may have to find new ways to do day to day tasks like traveling to work.
This examination really drove home a sentiment of how climate change impacts may most effect those who are more vulnerable. Here we see the more vulnerable community dealing with poverty and transportation issues taking more damage than an immediate adjacent community who appears to be better equipped to handle losses. I was surprised at how easily i found a clear example of this idea in this assignment.
Overall, while this analysis is beneficial, it has a flaw in that it is only evaluating one set of flood levels. While severity of storms is captured by considering 3 return periods, in reality, with the effects of climate change and increasing weather extremes, the floods which occur at 1, 20, and 100 year return period may increase over time. This means that our flood estimate for 2050, may be an underestimate of what is the mean 1, 20, and 100 year flood at that time. This question presents another layer of uncertainty which is very hard to predict, but may be an interesting element to add to future analyses.
Code developed in coordination with Awoe Mauna-Woanya and Bella Raja